Data and MLOps Manager

Uplers

Website: uplers.com
Job details:
Experience: 5.00 + years

Salary: INR 2000000-3500000 / year (based on experience)

Expected Notice Period: 15 Days

Shift: (GMT+05:30) Asia/Kolkata (IST)

Opportunity Type: Hybrid ()

Placement Type: Full Time Permanent position(Payroll and Compliance to be managed by: MDMY)

(*Note: This is a requirement for one of Uplers' client - MDMY)

What do you need for this opportunity?

Must have skills required:

Familiarity with industrial IoT, or Hme-series data, sensor data, and scalable workflows, and systemaHc problem-solving, Can balance 50% operaHons management and 50% hands-on execuHon (shijing to 70% management as team scales), Data OperaHons, Have 2+ years with team lead/management responsibility, Have 5-7+ years in MLOps, or AnalyHcs Engineering with hands-on ML operaHons experience, Process-driven mindset: create systems, SOPs, Value operaHonal excellence, zero-defect execuHon

MDMY is Looking for:

Our Data team is at an inflecTIon point. We have 13+ enterprise clients generating significant operational

workload, WITH_REPLACED operations consuming 40%+ of Director Hme (should be 10%). Our scaling constraint: we

need to go from current ML model production to 30+ models daily by 2027.

We need someone who can transform operational chaos into systematic excellence while scaling ML

model producHon 10x.

As Data + MLOps Manager, you''ll be the operational backbone of our Data team - freeing the Director

from 20-25 hours/week of operational work while building world-class ML operations from the ground

up. You''ll manage a team of 2-3 iniHally (growing to 6-7), establish operational discipline for ML model

lifecycle management, and ensure every client dataset entering our pla`orm meets our zero-defect

standard.

You''re an ideal candidate if you:

• Have 5-7+ years in MLOps, Data Operations, or Analytics Engineering WITH_REPLACED hands-on ML
  
  
  

operations experience

• Can balance 50% operations management and 50% hands-on execution (shijing to 70%
  
  
  

management as team scales)

• Have 2+ years WITH_REPLACED team lead/management responsibility
  
  
  

Process-driven mindset: create systems, SOPs, and scalable workflows

• Value operational excellence, zero-defect execution, and systemaTIc problem-solving
  
  
  

What We Offer

• High-impact role: Scale ML operaTIons 10x (current pace to 30+ models daily by 2027)
• Ownership culture: Build ML + data operaTIons funcTIon from scratch
• Career growth: Expand team, potenTIal path to Head of MLOps level
• Learning environment: Work WITH_REPLACED world-class turbomachinery and data experts
• Hybrid flexibility: 2-3 days on-site, rest remote
• Cukng-edge ML: Work WITH_REPLACED AutoML, model automaHon, and scaling challenges
  
  
  

Role Overview

As Data + MLOps Manager , you''ll:

• Own operaTIonal excellence across ML model lifecycle and data operaTIons
• Manage team of 2-3 (growing to 6-7) and distribute operaTIonal workload
• Establish SLAs and build automaTIon to reduce manual burden by 40%+
• Scale ML model producTIon from current pace to 30+ models daily by 2027
• Own end-to-end ML operaTIons: training, validaHon, deployment, monitoring
• Lead client data onboarding and ensure zero-defect data quality
• Free Director from 20-25 hours/week of operaTIons firefighting
  
  
  

Your success = Director shifts from 40% operational to 90% strategic focus while ML productIon scales

10x

Key Responsibilities

• Operations Management & Process Excellence (30%)
  
  
  

Own the operaTIonal workload that''s currently bottlenecking the team:

• Distribute and manage operaTIonal work across team (ML model creaTIon, data onboarding, ad-
  
  
  

hoc requests)

• Triage incoming requests and delegate appropriately (what''s urgent vs what can wait)
• Establish SLAs for ML operaTIons and data operaTIons requests
• Build processes and automaTIon to reduce manual operaTIonal burden by 40%+
• Capacity planning: ensure team can scale from current pace to 30+ models daily by 2027
• IdenTIfy operaTIonal bottlenecks and implement systemaTIc soluTIons
• Shield the team from chaos through structure and clear ownership
  
  
  

Primary Goal: Free Director from operaTIons firefighTIng, enabling 90% strategic focus

• ML Operations Management (25%)
  
  
  

Own end-to-end ML model lifecycle (hands-on iniTIally, then managing team):

• Use AutoML tools to train regression models for clients
• Validate models against new data and ensure quality standards
• Deploy models to producTIon environments
• Monitor model performance and detect drij
• Manage model retraining schedules and lifecycle
• Build automaTIon for model monitoring (currently manual scripts)
• Coordinate WITH_REPLACED ML engineers on model improvements and debugging
• TransiTIon from 80% manual ML ops to automated, scalable processes
  
  
  

Key Metric: Scale ML model producHon capacity 10x while maintaining quality

• Data Onboarding & Client Operations (25%)
  
  
  

Own end-to-end client data ingesTIon and ML-readiness (hands-on iniTIally, then oversight):

• Lead client dataset onboarding from raw data to ML-ready state
• Prepare data for ML model training using AutoML pla`orm
• Write and opTImize SQL queries to inspect, transform, and validate client data
• Implement rigorous DQA workflows: type checks, missingness, outliers, reconciliaTIon
• Partner WITH_REPLACED Customer Success, Product, and Engineering to resolve blockers
• Ensure zero defects in client data entering ML pipelines
• Team Leadership & Hiring (20%)
  
  
  

Scale and manage the ML/Data operaTIons team:

• Directly manage 2-3 people iniTIally, grow team to 6-7 over next 12-18 months
• Conduct weekly 1:1s, performance reviews, career development planning
• Hire and onboard 2x ML/Data Ops Specialists WITH_REPLACED Director approval
• Create SOPs, training materials, and knowledge transfer processes
• Foster culture of rigor, craftsmanship, and zero-defect execuTIon
• Ability to assess technical candidates and build a team
  
  
  

Core Technical Skills

• Programming
• Strong proficiency in Python (Pandas, NumPy, Polars)
• Ability to write production-quality code
• Nice to have: Experience WITH_REPLACED ML frameworks such as scikit-learn and XGBoost
• SQL
• Ability to write optimized SQL queries for large datasets
• Experience WITH_REPLACED query tuning and performance optimization
• Nice to have: Knowledge of query optimization techniques, window functions, and CTEs
• ML Operations
• Experience WITH_REPLACED model training, validation, deployment, and monitoring workflows
• Nice to have: Experience WITH_REPLACED AutoML platforms and MLOps tools such as MLflow and Ray
• Data Quality
• Experience in data validation and cleaning
• Ability to perform anomaly detection
• Experience building automated data quality workflows
• Nice to have: Statistical methods for outlier detection
• Automation
• Scripting for process automation
• Experience WITH_REPLACED scheduling and orchestration
• Nice to have: Experience WITH_REPLACED Airflow, Prefect, or Dagster
• ML Understanding
• Strong understanding of ML concepts including regression, classification, drift, and evaluation metrics
• Nice to have: Hands-on ML model training experience
• Leadership
• 2+ years of team lead or management responsibility
• Nice to have: Experience managing both data and ML operations teams
  
  
  

Experience & Attributes

• 5-7+ years in ML OperaTIons, Data OperaTIons, AnalyTIcs Engineering, MLOps, or similar roles
• 2+ years WITH_REPLACED team lead/management responsibility
• Strong hands-on experience WITH_REPLACED ML model lifecycle (training, deployment, monitoring)
• Process-driven mindset: Create systems, SOPs, and scalable workflows
• Ability to assess technical candidates and build a team
• Hands-on to hands-off transiTIon: Comfortable starTIng hands-on and evolving to management
• Experience in fast-paced, high-growth startup environments preferred
• Track record building operaTIonal automaHon that reduces manual work 40%+
• Experience scaling ML producTIon from low volume to high volume (10x+ growth)
• Familiarity WITH_REPLACED industrial IoT, sensor data, or Hme-series data
  
  
  

Qualifications

• Bachelor''s degree in Engineering, Computer Science, MathemaTIcs, StaHsHcs, Data Science, or
  
  
  

equivalent

• Strong foundaTIon in data structures, algorithms, and ML fundamentals
• Startup or high-growth environment experience strongly preferred
  
  
  

How to apply for this opportunity?

• Step 1: Click On Apply! And Register or Login on our portal.
• Step 2: Complete the Screening Form & Upload updated Resume
• Step 3: Increase your chances to get shortlisted & meet the client for the Interview!
  
  
  

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